We follow [1]} to compute the amount of information captured by an edge about the training data. Recall from Section  that for node \(v_j^l\)  of layer \(l\) , the activation is given as \(\mathbf {a}_j^{l} = g\left(\sum _{i=1}^{n_{l-1}} w_{ij}^{l-1} \mathbf {a}_i^{l-1} \right),\)  where \(w_{ij}^{l-1}\)  is the weight of the edge from \(i^{th}\)  node of layer \(l-1\)  to \(j^{th}\)  node of layer \(l\) .
Hence the information carried from layer \(l-1\)  to node \(v_{j}^{l}\)  of layer \(l\)  through the edge \(e_{ij}^{l-1}\)  is \(w_{ij}^{l-1} \mathbf {a}_i^{l-1}.\)  We choose a subset of incoming edges for node \(v_{j}^{l}\)  such that it preserves diverse information about the input. The DPP kernel \(\mathbf {L}^j\)  for the set of incoming edges of \(v_j^l\)  is defined as,
